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WO2020123724A4 - Machine learning systems and methods for assessment, healing prediction, and treatment of wounds - Google Patents

Machine learning systems and methods for assessment, healing prediction, and treatment of wounds Download PDF

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Publication number
WO2020123724A4
WO2020123724A4 PCT/US2019/065820 US2019065820W WO2020123724A4 WO 2020123724 A4 WO2020123724 A4 WO 2020123724A4 US 2019065820 W US2019065820 W US 2019065820W WO 2020123724 A4 WO2020123724 A4 WO 2020123724A4
Authority
WO
WIPO (PCT)
Prior art keywords
pixels
wound
subset
healing
wavelength
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/US2019/065820
Other languages
French (fr)
Other versions
WO2020123724A1 (en
Inventor
Wensheng Fan
John Michael Dimaio
Jeffrey E. Thatcher
Peiran QUAN
Faliu YI
Kevin PLANT
Ronald Baxter
Brian MCCALL
Zhicun Gao
Jason DWIGHT
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Spectral MD Inc
Original Assignee
Spectral MD Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority to BR112021011132-8A priority Critical patent/BR112021011132A2/en
Priority to CN201980087443.XA priority patent/CN113260303B/en
Priority to KR1020247036978A priority patent/KR20240163190A/en
Priority to KR1020217021623A priority patent/KR102728475B1/en
Priority to JP2021533805A priority patent/JP7261883B2/en
Priority to EP19894740.0A priority patent/EP3893733A4/en
Application filed by Spectral MD Inc filed Critical Spectral MD Inc
Priority to US16/738,911 priority patent/US10783632B2/en
Publication of WO2020123724A1 publication Critical patent/WO2020123724A1/en
Publication of WO2020123724A4 publication Critical patent/WO2020123724A4/en
Priority to US17/013,336 priority patent/US11599998B2/en
Anticipated expiration legal-status Critical
Priority to US18/177,493 priority patent/US11948300B2/en
Priority to JP2023063250A priority patent/JP7574354B2/en
Priority to US18/620,830 priority patent/US12444050B2/en
Priority to JP2024180995A priority patent/JP2025013851A/en
Ceased legal-status Critical Current

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Abstract

Machine learning systems and methods are disclosed for prediction of wound healing, such as for diabetic foot ulcers or other wounds, and for assessment implementations such as segmentation of images into wound regions and non-wound regions. Systems for assessing or predicting wound healing can include a light detection element configured to collect light of at least a first wavelength reflected from a tissue region including a wound, and one or more processors configured to generate an image based on a signal from the light detection element having pixels depicting the tissue region, determine reflectance intensity values for at least a subset of the pixels, determine one or more quantitative features of the subset of the plurality of pixels based on the reflectance intensity values, and generate a predicted or assessed healing parameter associated with the wound over a predetermined time interval.

Claims

AMENDED CLAIMS received by the International Bureau on 19 June 2020 (19.06.2020)What is claimed is:
1. A system for assessing or predicting wound healing, the system comprising:
at least one light detection element configured to collect light of at least a first wavelength after being reflected from a tissue region comprising a diabetic foot ulcer; and
one or more processors in communication with the at least one light detection element and configured to:
receive a signal from the at least one light detection element, the signal representing light of the first wavelength reflected from the tissue region;
generate, based on the signal, an image having a plurality of pixels depicting the tissue region;
determine, based on the signal, a reflectance intensity value at the first wavelength for each pixel of at least a subset of the plurality of pixels; determine one or more quantitative features of the subset of the plurality of pixels based on the reflectance intensity values of each pixel of the subset; and
generate, using one or more machine learning algorithms, at least one scalar value based on the one or more quantitative features of the subset of the plurality of pixels, the at least one scalar value corresponding to a predicted amount of healing of the diabetic foot ulcer over a predetermined time interval following generation of the image.
2. The system of Claim 1, wherein the predicted amount of healing is a predicted percent area reduction of the diabetic foot ulcer.
3. The system of any of Claims 1 or 2, wherein the at least one scalar value comprises a plurality of scalar values, each scalar value of the plurality of scalar values corresponding to a probability of healing of an individual pixel of the subset or of a subgroup of individual pixels of the subset.
4. The system of Claim 3, wherein the one or more processors are further configured to output a visual representation of the plurality of scalar values for display to a user.
91
5. The system of Claim 4, wherein the visual representation comprises the image having each pixel of the subset displayed with a particular visual representation selected based on the probability of healing corresponding to the pixel, wherein pixels associated with different probabilities of healing are displayed in different visual representations.
6. The system of any of Claims 3-5, wherein the one or more machine learning algorithms comprise a SegNet pre-trained using a wound, burn, or ulcer image database.
7. The system of Claim 6, wherein the wound image database comprises a diabetic foot ulcer image database.
8. The system of Claim 6 or 7, wherein the wound image database comprises a bum image database.
9. The system of any of Claims 1-8, wherein the predetermined time interval is 30 days.
10. The system of any of Claims 1-9, wherein the one or more processors are further configured to identify at least one patient health metric value corresponding to a patient having the tissue region, and wherein the at least one scalar value is generated based on the one or more quantitative features of the subset of the plurality of pixels and on the at least one patient health metric value.
11. The system of Claim 10, wherein the at least one patient health metric value comprises at least one variable selected from the group consisting of demographic variables, diabetic foot ulcer history variables, compliance variables, endocrine variables, cardiovascular variables, musculoskeletal variables, nutrition variables, infectious disease variables, renal variables, obstetrics or gynecology variables, drug use variables, other disease variables, or laboratory values.
12. The system of Claim 10, wherein the at least one patient health metric value comprises one or more clinical features.
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13. The system of Claim 12, wherein the one or more clinical features comprise at least one feature selected from the group consisting of an age of the patient, a level of chronic kidney disease of the patient, a length of the diabetic foot ulcer on a day when the image is generated, and a width of the diabetic foot ulcer on the day when the image is generated.
14. The system of any of Claims 1-13, wherein the first wavelength is within the range of 420 nm ± 20 nm, 525 nm ± 35 nm, 581 nm ± 20 nm, 620 nm ± 20 nm, 660 nm ± 20 nm, 726 nm ± 41 nm, 820 nm ± 20 nm, or 855 nm ± 30 nm.
15. The system of any of Claims 1-14, wherein the first wavelength is within the range of 620 nm ± 20 nm, 660 nm ± 20 nm, or 420 nm ± 20 nm.
16. The system of Claim 15, wherein the one or more machine learning algorithms comprise a random forest ensemble.
17. The system of any of Claims 1-14, wherein the first wavelength is within the range of 726 nm ± 41 nm, 855 nm ± 30 nm, 525 nm ± 35 nm, 581 nm ± 20 nm, or 820 nm ± 20 nm.
18. The system of Claim 17, wherein the one or more machine learning algorithms comprise an ensemble of classifiers.
19. The system of any of Claims 1-18, further comprising an optical bandpass filter configured to pass light of at least the first wavelength.
20. The system of any of Claims 1-19, wherein the one or more processors are further configured to:
automatically segment the plurality of pixels of the image into wound pixels and non- wound pixels; and
select the subset of the plurality of pixels to comprise the wound pixels.
21. The system of Claim 20, wherein the one or more processors are further configured to automatically segment the non-wound pixels into callus pixels and background pixels.
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22. The system of Claim 20, wherein the one or more processors are further configured to automatically segment the non-wound pixels into callus pixels, normal skin pixels, and background pixels.
23. The system of any of Claims 20-22, wherein the one or more processors automatically segment the plurality of pixels using a segmentation algorithm comprising a convolutional neural network.
24. The system of Claim 23, wherein the segmentation algorithm is at least one of a U-Net comprising a plurality of convolutional layers and a SegNet comprising a plurality of convolutional layers.
25. The system of any of Claims 1-24, wherein the one or more quantitative features of the subset of the plurality of pixels comprise one or more aggregate quantitative features of the plurality of pixels.
26. The system of Claim 25, wherein the one or more aggregate quantitative features of the subset of the plurality of pixels are selected from the group consisting of a mean of the reflectance intensity values of the pixels of the subset, a standard deviation of the reflectance intensity values of the pixels of the subset, and a median reflectance intensity value of the pixels of the subset.
27. The system of any of Claims 1-26, wherein the one or more processors are further configured to:
individually apply a plurality of filter kernels to the image by convolution to generate a plurality of image transformations;
construct a 3D matrix from the plurality of image transformations; and determine one or more quantitative features of the 3D matrix, wherein the at least one scalar value is generated based on the one or more quantitative features of the subset of the plurality of pixels and on the one or more quantitative features of the 3D matrix.
28. The system of Claim 27, wherein the one or more quantitative features of the 3D matrix are selected from the group consisting of a mean of the values of the 3D
94 matrix, a standard deviation of the values of the 3D matrix, a median value of the 3D matrix, and a product of the mean and the median of the 3D matrix.
29. The system of Claim 28, wherein the at least one scalar value is generated based on the mean of the reflectance intensity values of the pixels of the subset, the standard deviation of the reflectance intensity values of the pixels of the subset, the median reflectance intensity value of the pixels of the subset, the mean of the values of the 3D matrix, the standard deviation of the values of the 3D matrix, and the median value of the 3D matrix.
30. The system of any of Claims 1-29, wherein the at least one light detection element is further configured to collect light of at least a second wavelength after being reflected from the tissue region, and wherein the one or more processors are further configured to:
receive a second signal from the at least one light detection element, the second signal representing light of the second wavelength reflected from the tissue region;
determine, based on the second signal, a reflectance intensity value at the second wavelength for each pixel of at least the subset of the plurality of pixels; and
determine one or more additional quantitative features of the subset of the plurality of pixels based on the reflectance intensity values of each pixel at the second wavelength;
wherein the at least one scalar value is generated based at least in part on the one or more additional quantitative features of the subset of the plurality of pixels.
95
31. A system for wound assessment, the system comprising:
at least one light detection element configured to collect light of at least a first wavelength after being reflected form a tissue region comprising a wound; and
one or more processors in communication with the at least one light detection element and configured to:
receive a signal from the at least one light detection element, the signal representing light of the first wavelength reflected from the tissue region;
generate, based on the signal, an image having a plurality of pixels depicting the tissue region;
determine, based on the signal, a reflectance intensity value at the first wavelength for each pixel of the plurality of pixels; and automatically segment, using a machine learning algorithm, individual pixels of the plurality of pixels into at least a first subset of the plurality of pixels comprising wound pixels and a second subset of the plurality of pixels comprising non-wound pixels, based on individual reflectance intensity values of the plurality of pixels.
32. The system of Claim 31, wherein the one or more processors are further configured to automatically segment the second subset of the plurality of pixels into at least two categories of non-wound pixels, the at least two categories selected from the group consisting of callus pixels, normal skin pixels, and background pixels.
33. The system of Claim 31 or 32, wherein the machine learning algorithm comprises a convolutional neural network.
34. The system of Claim 33, wherein the machine learning algorithm is at least one of a U-Net comprising a plurality of convolutional layers and a SegNet comprising a plurality of convolutional layers.
96
35. The system of any of Claims 31-34, wherein the machine learning algorithm is trained based on a dataset comprising a plurality of segmented images of wounds, ulcers, or bums.
36. The system of any of Claims 31-35, wherein the wound is a diabetic foot ulcer.
37. The system of any of Claims 31-36, wherein the one or more processors are further configured to output a visual representation of the segmented plurality of pixels for display to a user.
38. The system of Claim 37, wherein the visual representation comprises the image having each pixel displayed with a particular visual representation selected based on the segmentation of the pixel, wherein wound pixels and non-wound pixels are displayed in different visual representations.
39. A method of predicting wound healing using the system of any of Claims 1-30, the method comprising:
illuminating the tissue region with light of at least the first wavelength such that the tissue region reflects at least a portion of the light to the at least one light detection element;
using the system to generate the at least one scalar value; and determining the predicted healing parameter over the predetermined time interval.
40. The method of Claim 39, wherein illuminating the tissue region comprises activating one or more light emitters configured to emit light of at least the first wavelength.
41. The method of Claim 39, wherein illuminating the tissue region comprises exposing the tissue region to ambient light.
42. The method of any of Claims 39-41, wherein determining the predicted healing parameter comprises determining an expected percent area reduction of the wound over the predetermined time interval.
43. The method of any of Claims 39-42, further comprising:
measuring one or more dimensions of the wound after the predetermined time interval has elapsed following the determination of the predicted amount of healing of the wound;
determining an actual amount of healing of the wound over the predetermined time interval; and
updating at least one machine learning algorithm of the one or more machine learning algorithms by providing at least the image and the actual amount of healing of the wound as training data.
44. The method of any of Claims 39-43, further comprising selecting between a standard wound care therapy and an advanced wound care therapy based at least in part on the predicted healing parameter.
45. The method of Claim 44, wherein selecting between the standard wound care therapy and the advanced wound care therapy comprises:
when the predicted healing parameter indicates that the wound, preferably a DFU, will heal or close by greater than 50% in 30 days, indicating or applying one or more standard therapies selected from the group consisting of optimization of nutritional status, debridement by any means to remove devitalized tissue, maintenance of a clean moist bed of granulation tissue with appropriate moist dressings, necessary therapy to resolve any infection that may be present, addressing any deficiencies in vascular perfusion to the extremity with the DFU, offloading of pressure from the DFU, and appropriate glucose control; and
when the predicted healing parameter indicates that the wound, preferably a DFU, will not heal or close by greater than 50% in 30 days, indicating or applying one or more advanced care therapies selected from the group consisting of hyperbaric oxygen therapy, negative-pressure wound therapy, bioengineered skin substitutes, synthetic growth factors, extracellular matrix proteins, matrix metalloproteinase modulators, and electrical stimulation therapy.
PCT/US2019/065820 2018-12-14 2019-12-11 Machine learning systems and methods for assessment, healing prediction, and treatment of wounds Ceased WO2020123724A1 (en)

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BR112021011132-8A BR112021011132A2 (en) 2018-12-14 2019-12-11 MACHINE LEARNING SYSTEMS AND METHODS FOR WOUND ASSESSMENT, PREDICTION AND WOUND TREATMENT
CN201980087443.XA CN113260303B (en) 2018-12-14 2019-12-11 Machine learning system and method for assessing, healing predicting and treating wounds
KR1020247036978A KR20240163190A (en) 2018-12-14 2019-12-11 Machine learning systems and methods for assessment, healing prediction, and treatment of wounds
KR1020217021623A KR102728475B1 (en) 2018-12-14 2019-12-11 Machine learning systems and methods for assessing, predicting healing, and treating wounds
JP2021533805A JP7261883B2 (en) 2018-12-14 2019-12-11 Machine learning system for wound assessment, healing prediction and treatment
EP19894740.0A EP3893733A4 (en) 2018-12-14 2019-12-11 MACHINE LEARNING SYSTEMS AND METHODS FOR EVALUATION, PREDICTION OF HEALING AND TREATMENT OF WOUNDS
US16/738,911 US10783632B2 (en) 2018-12-14 2020-01-09 Machine learning systems and method for assessment, healing prediction, and treatment of wounds
US17/013,336 US11599998B2 (en) 2018-12-14 2020-09-04 Machine learning systems and methods for assessment, healing prediction, and treatment of wounds
US18/177,493 US11948300B2 (en) 2018-12-14 2023-03-02 Machine learning systems and methods for assessment, healing prediction, and treatment of wounds
JP2023063250A JP7574354B2 (en) 2018-12-14 2023-04-10 A machine learning system for wound assessment, healing prediction and treatment
US18/620,830 US12444050B2 (en) 2018-12-14 2024-03-28 Machine learning systems and methods for assessment, healing prediction, and treatment of wounds
JP2024180995A JP2025013851A (en) 2018-12-14 2024-10-16 A machine learning system for wound assessment, healing prediction and treatment

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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11304604B2 (en) 2014-10-29 2022-04-19 Spectral Md, Inc. Reflective mode multi-spectral time-resolved optical imaging methods and apparatuses for tissue classification
US11337643B2 (en) 2017-03-02 2022-05-24 Spectral Md, Inc. Machine learning systems and techniques for multispectral amputation site analysis
US11631164B2 (en) 2018-12-14 2023-04-18 Spectral Md, Inc. System and method for high precision multi-aperture spectral imaging
RU2835539C1 (en) * 2024-03-06 2025-02-26 Федеральное государственное автономное образовательное учреждение высшего образования "Пермский государственный национальный исследовательский университет" Method for assessing rate of healing of superficial wounds and device for its implementation
US12444050B2 (en) 2018-12-14 2025-10-14 Spectral Md, Inc. Machine learning systems and methods for assessment, healing prediction, and treatment of wounds

Families Citing this family (42)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US12109429B2 (en) 2015-07-28 2024-10-08 Know Bio, Llc Phototherapeutic light for treatment of pathogens
EP3328491A4 (en) 2015-07-28 2019-05-01 PhotonMD, Inc. SYSTEMS AND METHODS FOR PHOTOTHERAPEUTIC MODULATION OF NITRIC OXIDE
US10783632B2 (en) 2018-12-14 2020-09-22 Spectral Md, Inc. Machine learning systems and method for assessment, healing prediction, and treatment of wounds
US10740884B2 (en) 2018-12-14 2020-08-11 Spectral Md, Inc. System and method for high precision multi-aperture spectral imaging
WO2020163539A1 (en) * 2019-02-05 2020-08-13 University Of Virginia Patent Foundation System and method for fully automatic lv segmentation of myocardial first-pass perfusion images
US20220133215A1 (en) * 2019-02-28 2022-05-05 FotoFinder Systems GmbH Method for evaluating skin lesions using artificial intelligence
US12447354B2 (en) 2020-03-19 2025-10-21 Know Bio, Llc Illumination devices for inducing biological effects
US11986666B2 (en) 2020-03-19 2024-05-21 Know Bio, Llc Illumination devices for inducing biological effects
US20220212031A1 (en) * 2020-03-19 2022-07-07 Know Bio, Llc Devices and systems for implementing therapeutic treatments of light
US12198809B2 (en) * 2020-06-19 2025-01-14 Neil Reza Shadbeh Evans Machine learning algorithms for detecting medical conditions, related systems, and related methods
EP4178426A4 (en) * 2020-07-13 2024-07-31 Spectral MD, Inc. SPECTRAL IMAGING SYSTEMS AND METHODS FOR HISTOLOGICAL ASSESSMENT OF WOUNDS
US12347337B2 (en) 2020-12-10 2025-07-01 Know Bio, Llc Enhanced testing and characterization techniques for phototherapeutic light treatments
WO2022197826A1 (en) * 2021-03-16 2022-09-22 Cornell University Systems and methods for using deep-learning algorithms to facilitate decision making in gynecologic practice
DE102021113153A1 (en) * 2021-05-20 2022-11-24 cureVision GmbH Mobile documentation device for recording skin lesions
US20220398820A1 (en) * 2021-06-11 2022-12-15 University Of Southern California Multispectral biometrics system
KR102613052B1 (en) 2021-09-29 2023-12-12 삼성전자주식회사 Multispectral image sensor and electronic apparatus including the image sensor
WO2023054768A1 (en) * 2021-09-30 2023-04-06 주식회사 피플앤드테크놀러지 Deep learning model system for detecting pressure ulcer disease and determining stage of pressure ulcer disease, generation method therefor, and method for diagnosing pressure ulcer by using same
CN113729638A (en) * 2021-09-30 2021-12-03 广州尚衡信息科技有限公司 Double-spectrum health assessment equipment for hand and face diagnosis
CN113827234B (en) * 2021-11-25 2022-03-18 之江实验室 A remote pulse wave reconstruction method based on hyperspectral face video
KR102713994B1 (en) 2022-01-18 2024-10-08 국립부경대학교 산학협력단 System and Method for detecting scar tissue area by machine learning
ES2948438A1 (en) * 2022-02-16 2023-09-12 Woom Fertility S L Computer-implemented method to obtain the fertility status of a woman (Machine-translation by Google Translate, not legally binding)
KR102781515B1 (en) * 2022-04-08 2025-03-14 (주)파인헬스케어 Apparatus for curing and determining pressure sore status in hospital and operating method thereof
EP4540831A1 (en) * 2022-06-14 2025-04-23 Solventum Intellectual Properties Company Artificial intelligence techniques for generating a predicted future image of a wound
CN115072423B (en) * 2022-06-27 2025-02-14 无锡快仓智能科技有限公司 Docking device and docking method
CN115187557A (en) * 2022-07-19 2022-10-14 成都泽康智骨科技有限公司 Spine interbody fusion detection device and method based on deep learning
KR102835008B1 (en) * 2022-08-31 2025-07-17 주식회사 인셉션랩 Non-contact data-based pressure ulcer diagnosis and prediction system using visual intelligence
WO2024263797A1 (en) * 2023-06-22 2024-12-26 Wound Pros Technology, Inc. Methods and systems for improving wound healing
CN116831528A (en) * 2023-06-29 2023-10-03 北京理工大学 A method for predicting diabetic foot based on multi-scale information fusion using hyperspectral reconstruction
US12437842B2 (en) 2023-09-28 2025-10-07 Vionix Biosciences Inc. System and method for analyzing spectral data using artificial intelligence
US20250142186A1 (en) * 2023-10-31 2025-05-01 Karl Storz Imaging, Inc. Dual magnification fluorescence imaging camera
CN117274242B (en) * 2023-11-17 2024-01-26 简阳市人民医院 Wound surface detection method and system based on image recognition
US20250166837A1 (en) * 2023-11-20 2025-05-22 Worcester Polytechnic Institute Wound assessment and classification
CN117854731B (en) * 2024-03-07 2024-05-17 简阳市人民医院 Prediction method and system for delayed wound healing influence factors after bromhidrosis operation
CN117877691B (en) * 2024-03-13 2024-05-07 四川省医学科学院·四川省人民医院 An intelligent wound information collection system based on image recognition
KR102727591B1 (en) * 2024-03-28 2024-11-11 주식회사 이노야드 Method for generating boundary and wound information of wound objects based on image processing
KR102707832B1 (en) * 2024-03-28 2024-09-25 주식회사 이노야드 Image processing-based 3d modeling of face or body with wounds and wound object extraction method
KR102771267B1 (en) * 2024-08-30 2025-02-20 김충현 Method for calculating ear-keloid recurrence rate after treatment
CN119339149B (en) * 2024-10-23 2025-07-22 武汉长江激光科技有限公司 Strong pulse light skin beauty local area identification method and system
CN119810746B (en) * 2024-12-23 2025-11-28 浙江大学 Clinical wound healing monitoring method based on three-dimensional wound assessment tool
CN119540246B (en) * 2025-01-23 2025-04-08 中国人民解放军空军军医大学 Craniocerebral trauma prognosis analysis system and method based on three-dimensional model
CN120507088B (en) * 2025-05-06 2025-12-09 广西科技师范学院 Soft package tightness detection method and system based on image recognition
CN120189085B (en) * 2025-05-23 2025-11-07 合肥全色光显科技有限公司 Imaging type health index detection device

Family Cites Families (176)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4170987A (en) 1977-11-28 1979-10-16 California Institute Of Technology Medical diagnosis system and method with multispectral imaging
US4693255A (en) 1985-04-22 1987-09-15 Beall Harry C Medical apparatus method for assessing the severity of certain skin traumas
DE69029275T2 (en) 1989-12-14 1997-06-12 Eriksson Elof TREATMENT SYSTEM FOR Wounds and other faults
US5074306A (en) 1990-02-22 1991-12-24 The General Hospital Corporation Measurement of burn depth in skin
US5701902A (en) 1994-09-14 1997-12-30 Cedars-Sinai Medical Center Spectroscopic burn injury evaluation apparatus and method
GB9521784D0 (en) 1995-10-24 1996-01-03 Rosslyn Medical Ltd Diagnostic apparatus
US6081612A (en) 1997-02-28 2000-06-27 Electro Optical Sciences Inc. Systems and methods for the multispectral imaging and characterization of skin tissue
US6008889A (en) 1997-04-16 1999-12-28 Zeng; Haishan Spectrometer system for diagnosis of skin disease
US6058352A (en) 1997-07-25 2000-05-02 Physical Optics Corporation Accurate tissue injury assessment using hybrid neural network analysis
US6353753B1 (en) 1998-05-05 2002-03-05 Stephen Thomas Flock Optical imaging of deep anatomic structures
US6352517B1 (en) 1998-06-02 2002-03-05 Stephen Thomas Flock Optical monitor of anatomical movement and uses thereof
US5982497A (en) 1998-07-09 1999-11-09 Optical Insights, Llc Multi-spectral two-dimensional imaging spectrometer
DE19850350C1 (en) 1998-11-02 2000-09-28 Jena Optronik Gmbh Method and device for generating data for the diagnosis of the degree of damage to a patient's skin tissue
US6381488B1 (en) 1999-06-15 2002-04-30 Sandia Corporation Method and apparatus to measure the depth of skin burns
US6640132B1 (en) 1999-11-17 2003-10-28 Hypermed, Inc. Forensic hyperspectral apparatus and method
CA2398278C (en) 2000-01-27 2012-05-15 National Research Council Of Canada Visible-near infrared spectroscopy in burn injury assessment
AU2001259435A1 (en) 2000-05-03 2001-11-12 Stephen T Flock Optical imaging of subsurface anatomical structures and biomolecules
US6638668B2 (en) 2000-05-12 2003-10-28 Ocean Optics, Inc. Method for making monolithic patterned dichroic filter detector arrays for spectroscopic imaging
CN1543325A (en) 2001-07-16 2004-11-03 ART�Ƚ��о��Ƽ���˾ Multi-wavelength imaging of highly turbid media
MXPA04002498A (en) 2001-09-19 2005-04-11 Joule Microsystems Canada Inc A spectrometer incorporating signal matched filtering.
US20060072109A1 (en) 2004-09-03 2006-04-06 Andrew Bodkin Hyperspectral imaging systems
TW512058B (en) 2001-12-24 2002-12-01 Yung-Jian Yang Spectral analysis system for burning and scalding injuries and device used in the system
CA2491703A1 (en) 2002-07-09 2004-01-15 Medispectra, Inc. Methods and apparatus for characterization of tissue samples
DE60314282T2 (en) 2002-12-02 2008-03-20 River Diagnostics B.V. USE OF HIGH-WAVE RAMAN SPECTROSCOPY FOR TISSUE MEASUREMENT
WO2005002425A2 (en) 2003-07-02 2005-01-13 U.S. Government As Represented By The Secretary Of The Army Wearable tissue viability diagnostic unit
US7693069B2 (en) 2003-07-28 2010-04-06 Alcatel-Lucent Usa Inc. Method, apparatus and system for improved inter-domain routing convergence
RU2372117C2 (en) 2003-09-18 2009-11-10 Аркюо Медикал, Инк. Method of opto-thermo-mechanical impact onto biological tissue and device to this end
US8634607B2 (en) * 2003-09-23 2014-01-21 Cambridge Research & Instrumentation, Inc. Spectral imaging of biological samples
US7920908B2 (en) 2003-10-16 2011-04-05 David Hattery Multispectral imaging for quantitative contrast of functional and structural features of layers inside optically dense media such as tissue
WO2005046248A1 (en) 2003-11-11 2005-05-19 Olympus Corporation Multi-spectrum image pick up device
US7433042B1 (en) 2003-12-05 2008-10-07 Surface Optics Corporation Spatially corrected full-cubed hyperspectral imager
US7648808B2 (en) 2004-01-12 2010-01-19 Ocean Thin Films, Inc. Patterned coated dichroic filter
US8583216B2 (en) 2004-04-27 2013-11-12 University Of Utah Research Foundation Skin type assessment and nevi screening for skin cancer with a noninvasive, portable reflectance spectrophotometer
US20070179482A1 (en) 2004-05-07 2007-08-02 Anderson Robert S Apparatuses and methods to treat biological external tissue
SE0402576D0 (en) 2004-10-25 2004-10-25 Forskarpatent I Uppsala Ab Multispectral and hyperspectral imaging
US8548570B2 (en) 2004-11-29 2013-10-01 Hypermed Imaging, Inc. Hyperspectral imaging of angiogenesis
US8224425B2 (en) 2005-04-04 2012-07-17 Hypermed Imaging, Inc. Hyperspectral imaging in diabetes and peripheral vascular disease
JP5149015B2 (en) 2004-12-28 2013-02-20 ハイパーメツド・イメージング・インコーポレイテツド Hyperspectral / multispectral imaging in the determination, evaluation and monitoring of systemic physiology and shock
US7729750B2 (en) 2005-01-20 2010-06-01 The Regents Of The University Of California Method and apparatus for high resolution spatially modulated fluorescence imaging and tomography
US20060241495A1 (en) 2005-03-23 2006-10-26 Eastman Kodak Company Wound healing monitoring and treatment
US8971984B2 (en) 2005-04-04 2015-03-03 Hypermed Imaging, Inc. Hyperspectral technology for assessing and treating diabetic foot and tissue disease
CA2947613C (en) 2005-04-04 2019-11-05 Hypermed Imaging, Inc. Hyperspectral imaging in diabetes and peripheral vascular disease
US8106956B2 (en) 2005-06-27 2012-01-31 Nokia Corporation Digital camera devices and methods for implementing digital zoom in digital camera devices and corresponding program products
EP1952106B1 (en) 2005-11-04 2014-08-27 George Themelis System for multispectral imaging
CN101394782B (en) 2006-01-05 2014-03-05 弗吉尼亚大学专利基金会 System for evaluation of blood glucose variability in diabetes from self-monitoring data
JP4655991B2 (en) 2006-04-21 2011-03-23 カシオ計算機株式会社 Imaging apparatus, electronic zoom method, and program
US8644911B1 (en) 2006-06-30 2014-02-04 Hypermed Imaging, Inc. OxyVu-1 hyperspectral tissue oxygenation (HTO) measurement system
WO2008033909A2 (en) 2006-09-12 2008-03-20 The General Hospital Corporation Apparatus, probe and method for providing depth assessment in an anatomical structure
US20120321759A1 (en) 2007-01-05 2012-12-20 Myskin, Inc. Characterization of food materials by optomagnetic fingerprinting
WO2008086311A2 (en) 2007-01-05 2008-07-17 Myskin, Inc. System, device and method for dermal imaging
WO2008105370A1 (en) 2007-02-26 2008-09-04 Olympus Medical Systems Corp. Observing device and observing method
US8639309B2 (en) 2007-07-31 2014-01-28 J&M Shuler, Inc. Method and system for monitoring oxygenation levels of compartments and tissue
US7879020B1 (en) 2007-03-19 2011-02-01 The United States Of America As Represented By The Secretary Of The Army Decision-assist method for resuscitation of patients
JP2008283442A (en) 2007-05-10 2008-11-20 Olympus Corp Imaging apparatus
US9226661B2 (en) 2007-07-06 2016-01-05 Industrial Research Limited Laser speckle imaging systems and methods
US20090072142A1 (en) 2007-09-14 2009-03-19 Forensicare Incorporated Scanning system and techniques for medical and/or forensic assessment using the same
US20090118600A1 (en) 2007-11-02 2009-05-07 Ortiz Joseph L Method and apparatus for skin documentation and analysis
US8509879B2 (en) 2007-11-06 2013-08-13 The Regents Of The University Of California Apparatus and method for widefield functional imaging (WiFI) using integrated structured illumination and laser speckle imaging
US20100210931A1 (en) 2008-04-04 2010-08-19 Modulate Imaging Inc. Method for performing qualitative and quantitative analysis of wounds using spatially structured illumination
US8812083B2 (en) 2008-04-21 2014-08-19 Philadelphia Health & Education Corporation Methods of optically monitoring wound healing
WO2009135081A2 (en) 2008-04-30 2009-11-05 Board Of Regents, The University Of Texas System Integrated patient bed system
WO2010042253A2 (en) 2008-04-30 2010-04-15 Board Of Regents, The University Of Texas System An apparatus and method for noninvasive evalution of a target versus a non- target
JP2011521237A (en) 2008-05-20 2011-07-21 ユニバーシティー ヘルス ネットワーク Apparatus and method for imaging and monitoring based on fluorescent light
US20090318815A1 (en) 2008-05-23 2009-12-24 Michael Barnes Systems and methods for hyperspectral medical imaging
US8694266B2 (en) 2008-06-05 2014-04-08 The Regents Of The University Of Michigan Multimodal spectroscopic systems and methods for classifying biological tissue
US9572494B2 (en) 2008-08-12 2017-02-21 New Jersy Institute of Technology Method and apparatus for multi-spectral imaging and analysis of skin lesions and biological tissues
JP2010043979A (en) 2008-08-13 2010-02-25 Yuichi Kamata Spectral image measuring device
US8488863B2 (en) 2008-11-06 2013-07-16 Los Alamos National Security, Llc Combinational pixel-by-pixel and object-level classifying, segmenting, and agglomerating in performing quantitative image analysis that distinguishes between healthy non-cancerous and cancerous cell nuclei and delineates nuclear, cytoplasm, and stromal material objects from stained biological tissue materials
WO2010124372A1 (en) 2009-04-29 2010-11-04 Genomedx Biosciences, Inc. Systems and methods for expression-based classification of thyroid tissue
EP2267654A1 (en) 2009-06-23 2010-12-29 Research in Motion Limited Adjustment of sharpness during digital zoom in digital photography
CN101627902B (en) 2009-07-15 2011-12-28 深圳先进技术研究院 Low-power consumption and high-precision front processing module of photoelectric plethysmograph signal based on ambient light
WO2011021128A2 (en) 2009-08-20 2011-02-24 Koninklijke Philips Electronics N.V. Method and system for image analysis
EP2486539B1 (en) 2009-10-06 2016-09-07 Koninklijke Philips N.V. Method and system for obtaining a first signal for analysis to characterize at least one periodic component thereof
US9220412B2 (en) 2009-11-19 2015-12-29 Modulated Imaging Inc. Method and apparatus for analysis of turbid media via single-element detection using structured illumination
US9823127B2 (en) 2010-01-22 2017-11-21 Duke University Systems and methods for deep spectroscopic imaging of biological samples with use of an interferometer and spectrometer
US8692912B2 (en) 2010-01-25 2014-04-08 Pixelteq, Inc. Multi-spectral camera
US8488024B2 (en) 2010-02-10 2013-07-16 Panasonic Corporation Image capture device
US8294570B2 (en) 2010-02-24 2012-10-23 Clawson Jeffrey J Burn diagnostic and intervention tool for emergency dispatch
WO2011140536A1 (en) 2010-05-07 2011-11-10 Purdue Research Foundation Quantitative image analysis for wound healing assay
WO2012035783A1 (en) 2010-09-17 2012-03-22 パナソニック株式会社 Stereoscopic video creation device and stereoscopic video creation method
US20120078088A1 (en) 2010-09-28 2012-03-29 Point of Contact, LLC. Medical image projection and tracking system
BR112013018023B1 (en) 2011-01-19 2021-09-08 The Regents Of The University Of California APPARATUS AND SYSTEM FOR MONITORING AND METHOD FOR PERFORMING REAL-TIME MONITORING OF OXYGENATION BY PERFUSION OF PATIENT TARGET TISSUE REGION
US8761853B2 (en) 2011-01-20 2014-06-24 Nitto Denko Corporation Devices and methods for non-invasive optical physiological measurements
US8994819B2 (en) 2011-02-04 2015-03-31 Raytheon Company Integrated optical detection system
US8694067B2 (en) 2011-02-15 2014-04-08 General Electric Company Sensor, apparatus and method for non-invasively monitoring blood characteristics of a subject
EP2683328B1 (en) 2011-03-07 2017-11-08 Wake Forest University Health Sciences Delivery system
US9372118B1 (en) 2011-03-07 2016-06-21 Fluxdata, Inc. Apparatus and method for multispectral based detection
JP5704984B2 (en) 2011-03-22 2015-04-22 キヤノン株式会社 Imaging device
US20120288230A1 (en) 2011-05-13 2012-11-15 Kestrel Labs, Inc. Non-Reflective Optical Connections in Laser-Based Photoplethysmography
WO2012170963A1 (en) 2011-06-08 2012-12-13 Digital Light Innovations System and method for hyperspectral imaging
KR101813315B1 (en) 2011-06-24 2017-12-28 케이엘에이-텐코 코포레이션 Method and apparatus for inspection of light emitting semiconductor devices using photoluminescence imaging
US9270875B2 (en) 2011-07-20 2016-02-23 Broadcom Corporation Dual image capture processing
KR101134770B1 (en) 2011-07-22 2012-04-13 양완석 Portable exoscope with ccd camera for human body opening
US8942459B2 (en) 2011-09-12 2015-01-27 Perkinelmer Cellular Technologies Germany Gmbh Methods and apparatus for fast identification of relevant features for classification or regression
US9295402B1 (en) 2012-01-17 2016-03-29 University Of Washington Through Its Center For Commercialization Methods and systems for assessing a burn injury
US20150011892A1 (en) 2012-01-20 2015-01-08 Harvard Apparatus Regenerative Technology, Inc. Methods for evaluating tissue injuries
US20150044098A1 (en) * 2012-01-30 2015-02-12 Scanadu Incorporated Hyperspectral imaging systems, units, and methods
WO2013160861A1 (en) 2012-04-27 2013-10-31 Aïmago S.A. Optical coherent imaging medical device
US9031306B2 (en) 2012-05-02 2015-05-12 The Regents Of The University Of California Diagnostic and prognostic histopathology system using morphometric indices
US9593982B2 (en) 2012-05-21 2017-03-14 Digimarc Corporation Sensor-synchronized spectrally-structured-light imaging
RU2616653C2 (en) 2012-06-05 2017-04-18 Хайпермед Имэджинг, Инк. Methods and device for coaxial image forming with multiple wavelengths
US9766382B2 (en) 2012-06-05 2017-09-19 Hypermed Imaging, Inc. Single-sensor hyperspectral imaging device
US20130331669A1 (en) 2012-06-11 2013-12-12 Raytheon Company Multi-spectral imaging system and method for remote biometric measurement of human physiological parameters
US9036877B2 (en) 2012-06-20 2015-05-19 Xerox Corporation Continuous cardiac pulse rate estimation from multi-channel source video data with mid-point stitching
US9547178B2 (en) 2012-08-15 2017-01-17 Semrock. Inc. Dichroic image splitter
US9370533B2 (en) 2012-09-13 2016-06-21 Polyheal Ltd. Wound healing compositions comprising microspheres
MX392469B (en) 2012-11-07 2025-03-24 Modulated Imaging Inc Efficient modulated imaging
CN105103190B (en) 2013-01-10 2018-07-17 加利珀生命科学股份有限公司 Full visual field glass slide multi-optical spectrum imaging system and method
CN105102945B (en) 2013-01-10 2018-07-10 加利珀生命科学股份有限公司 Multi-optical spectrum imaging system and method
US20140213910A1 (en) 2013-01-25 2014-07-31 The Regents Of The University Of California Method and apparatus for performing qualitative and quantitative analysis of burn extent and severity using spatially structured illumination
CN103973957B (en) 2013-01-29 2018-07-06 上海八运水科技发展有限公司 Binocular 3D automatic focusing system for camera and method
JP6392785B2 (en) 2013-01-31 2018-09-19 ベンタナ メディカル システムズ， インコーポレイテッド System and method for calibrating, configuring, and determining validity of an imaging device or system for multiple tissue assays
US11653874B2 (en) * 2013-02-01 2023-05-23 Acceleritas Corporation Method and system for characterizing tissue in three dimensions using multimode optical measurements
EP2951301A4 (en) 2013-02-01 2016-11-02 Daniel Farkas Method and system for characterizing tissue in three dimensions using multimode optical measurements
GB201302451D0 (en) 2013-02-12 2013-03-27 Isis Innovation Method and system for signal analysis
AU2014224230A1 (en) * 2013-03-06 2015-09-24 Marika Pty Ltd Assessing optical density gradients and variations
WO2014143235A1 (en) 2013-03-14 2014-09-18 Integrated Plasmonics Corporation Ambient light assisted spectroscopy
CN112716471A (en) 2013-08-14 2021-04-30 佩德拉科技私人有限公司 System and method for assessing vascular remodeling
US9823128B2 (en) 2013-10-16 2017-11-21 Arizona Board Of Regents On Behalf Of The University Of Arizona Multispectral imaging based on computational imaging and a narrow-band absorptive filter array
CN103815875B (en) 2013-10-28 2015-06-03 重庆西南医院 Near-infrared spectrum imaging system for diagnosis of depth and area of burn skin necrosis
US10032287B2 (en) 2013-10-30 2018-07-24 Worcester Polytechnic Institute System and method for assessing wound
WO2015086911A1 (en) 2013-12-13 2015-06-18 Revenio Research Oy Medical imaging
EP3091897A4 (en) 2014-01-06 2017-12-27 The Florida International University Board of Trustees Near infrared optical imaging system for hemodynamic imaging, pulse monitoring, and mapping spatio-temporal features
US20150208923A1 (en) 2014-01-28 2015-07-30 The Texas A&M University System Non-Invasive Monitoring of Tissue Mechanical Properties
WO2015116823A1 (en) 2014-01-29 2015-08-06 The Johns Hopkins University System and method for wound imaging and debridement
WO2016178055A2 (en) 2014-02-07 2016-11-10 Santec Corporation Self-operated health monitoring device
DE102014002514B4 (en) 2014-02-21 2015-10-29 Universität Stuttgart Device and method for multi- or hyperspectral imaging and / or for distance and / or 2-D or 3-D profile measurement of an object by means of spectrometry
CN106461462B (en) 2014-03-21 2019-03-26 海佩尔梅德影像有限公司 Compact optical sensor
CA2890559C (en) 2014-05-01 2023-09-12 Rebellion Photonics, Inc. Dual-band divided aperture infra-red spectral imaging system
DE102014106974A1 (en) 2014-05-16 2015-11-19 Cubert GmbH Spatially and spectrally resolving hyperspectral camera and method
EP3151735A4 (en) 2014-06-04 2018-02-07 University of Massachusetts Medical School Hyperspectral imaging for prediction of skin injury after exposure to thermal energy or ionizing radiation
JP6728070B2 (en) 2014-06-05 2020-07-22 ウニベルジテート ハイデルベルク Method and means for multispectral imaging
CN115989999A (en) * 2014-07-24 2023-04-21 大学健康网络 Collection and analysis of data for diagnostic purposes
US10117617B2 (en) 2014-10-08 2018-11-06 Revealix, Inc. Automated systems and methods for skin assessment and early detection of a latent pathogenic bio-signal anomaly
JP6893877B2 (en) * 2014-10-29 2021-06-23 スペクトラル エムディー, インコーポレイテッドSpectral Md, Inc. Reflection Mode Multispectral Time-Resolved Optical Imaging Methods and Devices for Tissue Classification
US9717417B2 (en) 2014-10-29 2017-08-01 Spectral Md, Inc. Reflective mode multi-spectral time-resolved optical imaging methods and apparatuses for tissue classification
US20220142484A1 (en) 2014-10-29 2022-05-12 Spectral Md, Inc. Reflective mode multi-spectral time-resolved optical imaging methods and apparatuses for tissue classification
EP3228977A4 (en) 2014-12-01 2018-07-04 Sony Corporation Image-processing device and image-processing method
CN107209934B (en) 2014-12-03 2021-03-30 文塔纳医疗系统公司 Methods, systems and devices for quantitative analysis of heterogeneous biomarker distributions
US9990472B2 (en) 2015-03-23 2018-06-05 Ohio State Innovation Foundation System and method for segmentation and automated measurement of chronic wound images
US11037070B2 (en) 2015-04-29 2021-06-15 Siemens Healthcare Gmbh Diagnostic test planning using machine learning techniques
CA3241702A1 (en) 2015-05-29 2016-12-08 Rebellion Photonics, Inc. Hydrogen sulfide imaging system
EP3308209B1 (en) 2015-06-15 2020-05-27 Agrowing Ltd Multispectral imaging apparatus
WO2017026296A1 (en) 2015-08-10 2017-02-16 株式会社リコー Sample measurement apparatus
WO2017027839A1 (en) 2015-08-13 2017-02-16 Somagenics, Inc. Methods and compositions of short small hairpin rnas and micrornas for wound healing
US10921186B2 (en) 2015-09-22 2021-02-16 Hypermed Imaging, Inc. Methods and apparatus for imaging discrete wavelength bands using a mobile device
WO2017051230A1 (en) * 2015-09-23 2017-03-30 Novadaq Technologies Inc. Methods and systems for assessing healing of tissue
KR102634161B1 (en) 2015-10-28 2024-02-05 스펙트랄 엠디, 인크. Reflection mode multispectral time-resolved optical imaging methods and devices for tissue classification
NL2015804B1 (en) 2015-11-17 2017-06-02 Quest Photonic Devices B V Hyperspectral 2D imaging device.
US10572997B2 (en) 2015-12-18 2020-02-25 Given Imaging Ltd. System and method for detecting anomalies in an image captured in-vivo using color histogram association
WO2017181200A1 (en) 2016-04-15 2017-10-19 The Regents Of The University Of California Assessment of wound status and tissue viability via analysis of spatially resolved thz reflectometry maps
GB2550582B (en) * 2016-05-23 2020-07-15 Bluedrop Medical Ltd A skin inspection device identifying abnormalities
JP6585006B2 (en) 2016-06-07 2019-10-02 株式会社東芝 Imaging device and vehicle
US10013811B2 (en) 2016-06-13 2018-07-03 Umm-Al-Qura University Hyperspectral image visualization in patients with medical conditions
WO2017223206A1 (en) 2016-06-21 2017-12-28 Sri International Hyperspectral imaging methods and apparatuses
CN109843176A (en) * 2016-07-29 2019-06-04 诺瓦达克技术有限公司 Methods and systems for utilizing machine learning to characterize a subject's organization
US10223788B2 (en) 2016-08-31 2019-03-05 International Business Machines Corporation Skin lesion segmentation using deep convolution networks guided by local unsupervised learning
US10248713B2 (en) 2016-11-30 2019-04-02 Business Objects Software Ltd. Time series analysis using a clustering based symbolic representation
CN110114645B (en) 2016-12-27 2021-11-26 优鲁格斯股份有限公司 Hyperspectral imaging while the viewed object is stationary
US10145740B2 (en) 2017-02-27 2018-12-04 Visera Technologies Company Limited Sensing multiple peak wavelengths using combination of dual-band filters
US10806334B2 (en) 2017-02-28 2020-10-20 Verily Life Sciences Llc System and method for multiclass classification of images using a programmable light source
CN110573066A (en) 2017-03-02 2019-12-13 光谱Md公司 Machine Learning Systems and Techniques for Multispectral Amputation Site Analysis
US11003933B2 (en) 2017-08-15 2021-05-11 Noblis, Inc. Multispectral anomaly detection
WO2018217162A1 (en) 2017-10-17 2018-11-29 Kronikare Pte Ltd System and method for facilitating analysis of a wound in a target subject
CN111356909B (en) 2017-11-16 2023-07-04 多传感器科学公司 Systems and methods for multispectral imaging and gas detection using scanning illuminators and optical sensors
CN108053482A (en) 2018-02-05 2018-05-18 喻强 A kind of human body 3D modeling method based on mobile phone scanning
US20190290117A1 (en) 2018-03-22 2019-09-26 Kabushiki Kaisha Topcon Interferometric fundus imaging method
US11234666B2 (en) 2018-05-31 2022-02-01 Canon Medical Systems Corporation Apparatus and method for medical image reconstruction using deep learning to improve image quality in position emission tomography (PET)
BR112021011132A2 (en) 2018-12-14 2021-08-31 Spectral Md, Inc. MACHINE LEARNING SYSTEMS AND METHODS FOR WOUND ASSESSMENT, PREDICTION AND WOUND TREATMENT
WO2020123722A1 (en) 2018-12-14 2020-06-18 Spectral Md, Inc. System and method for high precision multi-aperture spectral imaging
US10740884B2 (en) 2018-12-14 2020-08-11 Spectral Md, Inc. System and method for high precision multi-aperture spectral imaging
US10783632B2 (en) 2018-12-14 2020-09-22 Spectral Md, Inc. Machine learning systems and method for assessment, healing prediction, and treatment of wounds
WO2021173763A1 (en) 2020-02-28 2021-09-02 Spectral Md, Inc. Machine learning systems and methods for assessment, healing prediction, and treatment of wounds
EP4178426A4 (en) 2020-07-13 2024-07-31 Spectral MD, Inc. SPECTRAL IMAGING SYSTEMS AND METHODS FOR HISTOLOGICAL ASSESSMENT OF WOUNDS
EP4314739A4 (en) 2021-03-30 2025-02-19 Spectral MD, Inc. System and method for high precision snapshot multi-spectral imaging based on multiplexed illumination
WO2023141216A2 (en) 2022-01-21 2023-07-27 Spectral Md, Inc. System and method for topological characterization of tissue

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11304604B2 (en) 2014-10-29 2022-04-19 Spectral Md, Inc. Reflective mode multi-spectral time-resolved optical imaging methods and apparatuses for tissue classification
US11337643B2 (en) 2017-03-02 2022-05-24 Spectral Md, Inc. Machine learning systems and techniques for multispectral amputation site analysis
US11631164B2 (en) 2018-12-14 2023-04-18 Spectral Md, Inc. System and method for high precision multi-aperture spectral imaging
US12444050B2 (en) 2018-12-14 2025-10-14 Spectral Md, Inc. Machine learning systems and methods for assessment, healing prediction, and treatment of wounds
RU2835539C1 (en) * 2024-03-06 2025-02-26 Федеральное государственное автономное образовательное учреждение высшего образования "Пермский государственный национальный исследовательский университет" Method for assessing rate of healing of superficial wounds and device for its implementation

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